ââåforecasting With Artificial Neural Networks the State of the Art
Questions tagged [neural-networks]
Artificial neural networks (ANNs) are a wide class of computational models loosely based on biological neural networks. They encompass feedforward NNs (including "deep" NNs), convolutional NNs, recurrent NNs, etc.
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What should I do when my neural network doesn't learn?
I'm preparation a neural network but the preparation loss doesn't decrease. How can I fix this? I'm non request most overfitting or regularization. I'm asking virtually how to solve the problem where my ...
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What should I do when my neural network doesn't generalize well?
I'thou training a neural network and the training loss decreases, but the validation loss doesn't, or it decreases much less than what I would expect, based on references or experiments with very similar ...
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How to cull the number of hidden layers and nodes in a feedforward neural network?
Is there a standard and accustomed method for selecting the number of layers, and the number of nodes in each layer, in a feed-frontwards neural network? I'm interested in automatic ways of building neural ...
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336 votes
v answers
353k views
What is the trade-off betwixt batch size and number of iterations to train a neural network?
When training a neural network, what difference does it make to set: batch size to $a$ and number of iterations to $b$ vs. batch size to $c$ and number of iterations to $d$ where $ ab = cd $? To ...
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What is the difference between a neural network and a deep neural network, and why exercise the deep ones work better?
I haven't seen the question stated precisely in these terms, and this is why I make a new question. What I am interested in knowing is not the definition of a neural network, but understanding the ...
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How can change in toll office be positive?
In chapter 1 of Nielsen's Neural Networks and Deep Learning it says To make gradient descent work correctly, nosotros need to choose the learning rate η to be minor enough that Equation (9) is a proficient ...
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Is it possible to train a neural network without backpropagation?
Many neural network books and tutorials spend a lot of time on the backpropagation algorithm, which is essentially a tool to compute the slope. Let's assume we are building a model with ~10K ...
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How to construct a cross-entropy loss for general regression targets?
It's common short-manus in neural networks literature to refer to categorical cross-entropy loss as simply "cross-entropy." However, this terminology is cryptic considering different probability ...
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Hateful or sum of gradients for weight updates in SGD
I am using single observation to compute losses using neural network implementation in PyTorch. I am confused in a pocket-sized detail of SGD. If I compute loss and do ...
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What are adept initial weights in a neural network?
I accept just heard, that it's a good idea to choose initial weights of a neural network from the range $(\frac{-1}{\sqrt d} , \frac{1}{\sqrt d})$, where $d$ is the number of inputs to a given neuron. ...
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How and why practise normalization and feature scaling work?
I see that lots of car learning algorithms work improve with hateful cancellation and covariance equalization. For example, Neural Networks tend to converge faster, and 1000-Means generally gives improve ...
- ane,427
Separate Models vs Flags in the same model
I have customer information from 2 brands. The data structure are the same, merely I expected the customer behaviour to be unlike in different brand. So I could train 2 models, 1 for each brand, or I could ...
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Should I use a categorical cross-entropy or binary cross-entropy loss for binary predictions?
First of all, I realized if I need to perform binary predictions, I have to create at to the lowest degree 2 classes through performing a i-hot-encoding. Is this correct? However, is binary cross-entropy simply ...
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Why are neural networks condign deeper, but not wider?
In recent years, convolutional neural networks (or perhaps deep neural networks in general) have become deeper and deeper, with country-of-the-art networks going from 7 layers (AlexNet) to g layers (...
- 5,999
117 votes
five answers
44k views
Comprehensive list of activation functions in neural networks with pros/cons
Are in that location any reference document(s) that give a comprehensive listing of activation functions in neural networks forth with their pros/cons (and ideally some pointers to publications where they were ...
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